from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-15 14:07:18.070317
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 15, Dec, 2020
Time: 14:07:21
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.6003
Nobs: 141.000 HQIC: -44.7176
Log likelihood: 1495.88 FPE: 1.77070e-20
AIC: -45.4825 Det(Omega_mle): 9.55708e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459135 0.175186 2.621 0.009
L1.Burgenland 0.147558 0.085021 1.736 0.083
L1.Kärnten -0.235185 0.068436 -3.437 0.001
L1.Niederösterreich 0.102927 0.206945 0.497 0.619
L1.Oberösterreich 0.247095 0.170440 1.450 0.147
L1.Salzburg 0.175724 0.087908 1.999 0.046
L1.Steiermark 0.097401 0.123588 0.788 0.431
L1.Tirol 0.143083 0.080953 1.767 0.077
L1.Vorarlberg 0.009099 0.078969 0.115 0.908
L1.Wien -0.128666 0.167028 -0.770 0.441
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.582455 0.229127 2.542 0.011
L1.Burgenland 0.010002 0.111199 0.090 0.928
L1.Kärnten 0.363988 0.089508 4.067 0.000
L1.Niederösterreich 0.140517 0.270665 0.519 0.604
L1.Oberösterreich -0.214835 0.222919 -0.964 0.335
L1.Salzburg 0.193437 0.114975 1.682 0.092
L1.Steiermark 0.237914 0.161642 1.472 0.141
L1.Tirol 0.142066 0.105879 1.342 0.180
L1.Vorarlberg 0.186149 0.103284 1.802 0.071
L1.Wien -0.620361 0.218456 -2.840 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.310292 0.074292 4.177 0.000
L1.Burgenland 0.100704 0.036055 2.793 0.005
L1.Kärnten -0.023930 0.029022 -0.825 0.410
L1.Niederösterreich 0.113403 0.087760 1.292 0.196
L1.Oberösterreich 0.285593 0.072279 3.951 0.000
L1.Salzburg -0.003182 0.037279 -0.085 0.932
L1.Steiermark -0.040474 0.052411 -0.772 0.440
L1.Tirol 0.090735 0.034330 2.643 0.008
L1.Vorarlberg 0.127599 0.033489 3.810 0.000
L1.Wien 0.047057 0.070832 0.664 0.506
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204442 0.086687 2.358 0.018
L1.Burgenland -0.004927 0.042071 -0.117 0.907
L1.Kärnten 0.021568 0.033864 0.637 0.524
L1.Niederösterreich 0.029101 0.102402 0.284 0.776
L1.Oberösterreich 0.400984 0.084338 4.754 0.000
L1.Salzburg 0.092553 0.043499 2.128 0.033
L1.Steiermark 0.194220 0.061155 3.176 0.001
L1.Tirol 0.030233 0.040058 0.755 0.450
L1.Vorarlberg 0.103015 0.039076 2.636 0.008
L1.Wien -0.071470 0.082650 -0.865 0.387
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.642665 0.184277 3.487 0.000
L1.Burgenland 0.077280 0.089433 0.864 0.388
L1.Kärnten 0.002922 0.071987 0.041 0.968
L1.Niederösterreich -0.079945 0.217684 -0.367 0.713
L1.Oberösterreich 0.129663 0.179285 0.723 0.470
L1.Salzburg 0.039337 0.092469 0.425 0.671
L1.Steiermark 0.125161 0.130002 0.963 0.336
L1.Tirol 0.218120 0.085154 2.561 0.010
L1.Vorarlberg 0.019611 0.083067 0.236 0.813
L1.Wien -0.154733 0.175695 -0.881 0.378
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.184744 0.127669 1.447 0.148
L1.Burgenland -0.032673 0.061960 -0.527 0.598
L1.Kärnten -0.012663 0.049873 -0.254 0.800
L1.Niederösterreich 0.171929 0.150813 1.140 0.254
L1.Oberösterreich 0.403300 0.124210 3.247 0.001
L1.Salzburg -0.029861 0.064063 -0.466 0.641
L1.Steiermark -0.042774 0.090066 -0.475 0.635
L1.Tirol 0.187141 0.058995 3.172 0.002
L1.Vorarlberg 0.035546 0.057549 0.618 0.537
L1.Wien 0.145812 0.121723 1.198 0.231
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.213803 0.160568 1.332 0.183
L1.Burgenland 0.083141 0.077926 1.067 0.286
L1.Kärnten -0.045142 0.062725 -0.720 0.472
L1.Niederösterreich -0.044439 0.189677 -0.234 0.815
L1.Oberösterreich -0.126242 0.156218 -0.808 0.419
L1.Salzburg 0.006945 0.080572 0.086 0.931
L1.Steiermark 0.391186 0.113276 3.453 0.001
L1.Tirol 0.519594 0.074198 7.003 0.000
L1.Vorarlberg 0.228441 0.072380 3.156 0.002
L1.Wien -0.224457 0.153090 -1.466 0.143
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.090461 0.186619 0.485 0.628
L1.Burgenland 0.035147 0.090569 0.388 0.698
L1.Kärnten -0.113983 0.072902 -1.564 0.118
L1.Niederösterreich 0.179344 0.220450 0.814 0.416
L1.Oberösterreich 0.015203 0.181563 0.084 0.933
L1.Salzburg 0.221217 0.093644 2.362 0.018
L1.Steiermark 0.155444 0.131653 1.181 0.238
L1.Tirol 0.085461 0.086236 0.991 0.322
L1.Vorarlberg 0.043287 0.084122 0.515 0.607
L1.Wien 0.298612 0.177928 1.678 0.093
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.596905 0.102880 5.802 0.000
L1.Burgenland -0.014505 0.049929 -0.291 0.771
L1.Kärnten -0.000828 0.040190 -0.021 0.984
L1.Niederösterreich -0.034442 0.121531 -0.283 0.777
L1.Oberösterreich 0.277733 0.100092 2.775 0.006
L1.Salzburg 0.005760 0.051625 0.112 0.911
L1.Steiermark 0.011676 0.072578 0.161 0.872
L1.Tirol 0.076218 0.047540 1.603 0.109
L1.Vorarlberg 0.183240 0.046375 3.951 0.000
L1.Wien -0.094936 0.098089 -0.968 0.333
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.130017 -0.012266 0.184216 0.238858 0.031395 0.081717 -0.127486 0.139807
Kärnten 0.130017 1.000000 -0.034897 0.170624 0.118183 -0.164655 0.164575 0.020930 0.289242
Niederösterreich -0.012266 -0.034897 1.000000 0.241568 0.056755 0.186111 0.085528 0.028596 0.358069
Oberösterreich 0.184216 0.170624 0.241568 1.000000 0.263742 0.272292 0.073285 0.048575 0.054436
Salzburg 0.238858 0.118183 0.056755 0.263742 1.000000 0.139943 0.052589 0.069511 -0.054580
Steiermark 0.031395 -0.164655 0.186111 0.272292 0.139943 1.000000 0.093926 0.059937 -0.170742
Tirol 0.081717 0.164575 0.085528 0.073285 0.052589 0.093926 1.000000 0.130913 0.101945
Vorarlberg -0.127486 0.020930 0.028596 0.048575 0.069511 0.059937 0.130913 1.000000 0.074193
Wien 0.139807 0.289242 0.358069 0.054436 -0.054580 -0.170742 0.101945 0.074193 1.000000